To conclude, the proposed evaluation metrics of the representation can debias and generalize representation learning because it is independent of the specific downstream tasks. As a follow-up work, [1]} even showed that when the network is trained using \(\mathcal {L}_{\text{align}}+\lambda \mathcal {L}_{\text{uniform}}\)  as a loss function, the weight \(\lambda \)  is inversely related to the temperature scaling \(\tau \)  used in contrastive loss.
